The popularity of digital images is rapidly increasing due to improving digital imaging technologies and easy availability facilitated by the Internet. More and more digital images are becoming available every day.
Automatic image retrieval systems provide an efficient way for users to navigate through the growing numbers of available images. Some existing conventional image retrieval systems catalog images by associating each image with one or more human-chosen keywords. One problem with these keyword-based image management systems is that it can be difficult or even impossible for a person to precisely describe the inherent complexity of certain images. As a result, retrieval accuracy can be severely limited because images that cannot be described or can only be described ambiguously will not be successfully retrieved. Another problem with keyword-based image management systems is that each image has to be manually inspected and carefully annotated. These steps are extremely labor intensive and prohibitively costly, especially for a database with a large number of images.
Recently, some image management systems that use content-based image retrieval (CBIR) have begun to emerge. Typically, a CBIR system is capable of identifying visual (i.e. non-semantic) features of a reference image and finding other images with those similar features. These visual features include color correlogram, color histogram, and wavelet features. To obtain these visual features of an image, substantial computational power is required in order to obtain meaningful and useful results.
Thus, there is a need for a CBIR system that employs visual features that are simple to calculate and capable of yielding accurate image retrieval results.